Particle Swarm Optimization (PSO)

Vehicle scheduling optimization of battery electric bus based on Particle Swarm Optimization algorithm

Introduction

   With the development of our society, more and more people choose public transport as their first choice for traveling because of environment friendly. And battery-electric buses are seen as a well-suited technology for the electrification of road-based public transport. However, the transition process from conventional diesel to electric buses faces major hurdles caused by range limitations and the required charging time of battery buses. </br>   So, how to optimize the vehicle scheduling of Battery Electric Buses needs to be studied. In my research, the optimization object including minimizing the total number of buses in operation within a day, minimizing charging cost and minimizing the battery cost. </br>   This research includes maintaining the health of batter, scheduling of electric buses and optimization of the charging process. In the paper, I choose two Battery Electric Buses lines which the data is from Shanghai Bus No. 3 Bus company for research. Based on the model I purposed and the Particle Swarm Optimization algorithm, I got optimization results with less use of buses and less charging time within a day. Last, I drew a Gantt chart of the battery-electric bus scheduling.

main_function.m

   Code of Partical Swarm Optimization Algorithm

Objective function

obj1.m

   Objective 1: To minimize the total cost of vehicle ownership.

obj2.m

   Objective 2: To minimize the total charging fee cost.

obj3.m

   Objective 3: To minimize the charging infrastructure cost.

Constraint

cons1.m

   Constraint 1: Each bus can only cover 2 times maximum trips in order to balance the usage count of each bus.

cons2.m

   Constraint 2: The continuous trip must be the up trip to down trip or down trip to up trip.

cons3.m

   Constraint 3: Energy consumption constraint.